Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources

Muneer Ahmed Salamkar

Abstract: Data integration, combining data from diverse sources into a unified view, has become a cornerstone of modern data-driven decision-making. However, the proliferation of data sources, formats, and platforms poses significant challenges. AI-driven approaches are revolutionizing this domain, offering innovative solutions to streamline integration processes. By leveraging machine learning algorithms, natural language processing, and pattern recognition, AI systems can efficiently identify relationships among disparate datasets, automate schema matching, and resolve conflicts in data formats. These methods enhance scalability, accuracy, and efficiency, enabling the integration of large volumes of structured and unstructured data with minimal human intervention. AI-driven tools enable real-time integration, providing businesses with up-to-date insights critical for maintaining a competitive edge. Furthermore, AI-powered metadata analysis and anomaly detection advancements enhance data quality and governance, addressing key concerns around consistency and compliance. This paper explores the methodologies underpinning AI-driven data integration, their applications across industries, and the remaining challenges, including ethical considerations and the need for robust training datasets. By analyzing AI's transformative impact on data integration, we highlight how organizations can harness these technologies to unlock the full potential of their data ecosystems and drive innovation.

Keywords: Data Integration, Artificial Intelligence, Machine Learning, Data Pipelines, ETL Processes